Instructions to use At-Tawheed/Anis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use At-Tawheed/Anis with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="At-Tawheed/Anis")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("At-Tawheed/Anis")
model = AutoModelForCausalLM.from_pretrained("At-Tawheed/Anis")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use At-Tawheed/Anis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "At-Tawheed/Anis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "At-Tawheed/Anis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/At-Tawheed/Anis

SGLang

How to use At-Tawheed/Anis with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "At-Tawheed/Anis" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "At-Tawheed/Anis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "At-Tawheed/Anis" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "At-Tawheed/Anis",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use At-Tawheed/Anis with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for At-Tawheed/Anis to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for At-Tawheed/Anis to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for At-Tawheed/Anis to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="At-Tawheed/Anis",
    max_seq_length=2048,
)

Docker Model Runner
How to use At-Tawheed/Anis with Docker Model Runner:
```
docker model run hf.co/At-Tawheed/Anis
```

Anis / README.md

At-Tawheed

Update README.md

97ba1fa verified about 2 months ago

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	---
	base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	- sft
	- rlhf
	- trl
	- conversational
	license: apache-2.0
	language:
	- en
	- multilingual
	datasets:
	- At-Tawheed/Anis-RLHF
	pipeline_tag: text-generation
	---

	# Anis

	Developed by: [At-Tawheed](https://huggingface.co/At-Tawheed) · Attawheed AI Lab (ATTLAB)
	Base model: [`unsloth/qwen2.5-7b-unsloth-bnb-4bit`](https://huggingface.co/unsloth/qwen2.5-7b-unsloth-bnb-4bit)
	Parameters: 8B · Tensor type: BF16 · License: Apache 2.0

	This Qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

	---

	## About Anis

	Anis is an 8B parameter language model fine-tuned from Qwen2.5-7B using Supervised Fine-Tuning (SFT). It is the first stage in ATTLAB's open-source RLHF alignment pipeline, trained on [`At-Tawheed/Anis-RLHF`](https://huggingface.co/datasets/At-Tawheed/Anis-RLHF) — a curated 57.9 GB dataset of 33 instruction, preference, math, code, and multilingual subsets.

	```
	unsloth/qwen2.5-7b-unsloth-bnb-4bit (base)
	└── Anis (SFT ← this model)
	└── attlab-7b-dpo-v1 (DPO)
	```

	System prompt:
	```
	You are ATTLAB, a helpful, harmless, and honest AI assistant developed by the ATTLAB team.
	```

	---

	## Training Data — Anis-RLHF (57.9 GB · 33 subsets)

	\| Category \| Subsets \|
	\|---\|---\|
	\| Instruction / Chat \| `openhermes_2_5`, `slim_orca`, `openorca_full`, `ultrachat_200k`, `smoltalk_1m`, `lmsys_chat_1m`, `tulu3_sft_mixture`, `oasst1_top_ranked` \|
	\| Preference / DPO \| `ultrafeedback`, `ultrafeedback_binarized`, `hh_rlhf_full`, `capybara_dpo_7k`, `helpsteer2` \|
	\| Math / Reasoning \| `metamath_qa`, `numina_math_cot`, `openmath_instruct2`, `magpie_reasoning_250k` \|
	\| Code \| `opencode_instruct_5m`, `codefeedback_66k`, `evol_codealpaca_110k`, `magicoder_oss_75k` \|
	\| Synthetic \| `magpie_llama3_1m`, `magpie_llama31_1m`, `magpie_llama33_1m`, `magpie_qwen25_1m` \|
	\| Knowledge \| `wikipedia_en`, `gutenberg_books`, `stackexchange_qa`, `fineweb_edu` \|
	\| Multilingual \| `aya_multilingual`, `wikipedia_yoruba` \|
	\| WizardLM \| `wizardlm_evol_v2` \|

	---

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = "At-Tawheed/Anis"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	messages = [
	{"role": "system", "content": "You are ATTLAB, a helpful, harmless, and honest AI assistant developed by the ATTLAB team."},
	{"role": "user", "content": "What is reinforcement learning from human feedback?"}
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	with torch.no_grad():
	outputs = model.generate(
	input_ids,
	max_new_tokens=256,
	do_sample=True,
	temperature=0.7,
	top_p=0.9,
	)

	response = outputs[0][input_ids.shape[-1]:]
	print(tokenizer.decode(response, skip_special_tokens=True))
	```

	### With Unsloth

	```python
	from unsloth import FastLanguageModel

	model, tokenizer = FastLanguageModel.from_pretrained(
	model_name="At-Tawheed/Anis",
	max_seq_length=2048,
	dtype=None,
	load_in_4bit=True,
	)
	FastLanguageModel.for_inference(model)
	```

	---

	## Limitations

	- SFT only: Anis is not fully aligned. For preference-optimized outputs use the DPO variant (`attlab-7b-dpo-v1`).
	- Hallucination: May produce factually incorrect outputs — do not use as a sole source of truth.
	- Bias: Training data is sourced from the internet and inherits its biases.

	---

	## Citation

	```bibtex
	@misc{anis2025,
	author = {Ibraheem, Olushola Taoheed},
	title = {Anis: A Supervised Fine-Tuned Language Model},
	year = {2025},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/At-Tawheed/Anis}},
	note = {Attawheed AI Lab (ATTLAB). Fine-tuned from Qwen2.5-7B with Unsloth and TRL.}
	}
	```

	---

	ATTLAB · [Hugging Face](https://huggingface.co/At-Tawheed) · [GitHub](https://github.com/OluOlamide) · [Dataset](https://huggingface.co/datasets/At-Tawheed/Anis-RLHF)